Running ‘testthat.R’ [202s/219s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
  > Sys.setenv("R_TESTS" = "")
  > 
  > library(testthat)
  > library(quanteda)
  quanteda version 0.9.9.50
  Using 23 of 24 cores for parallel computing
  
  Attaching package: 'quanteda'
  
  The following object is masked from 'package:utils':
  
      View
  
  > 
  > test_check("quanteda")
  OMP: Warning #96: Cannot form a team with 3 threads, using 2 instead.
  OMP: Hint: Consider unsetting KMP_ALL_THREADS and OMP_THREAD_LIMIT (if either is set).
     ... lowercasing
     ... indexing documents: 2 documents
     ... indexing features: 14 feature types
     ... applying a dictionary consisting of 6 keys
     ... created a 2 x 6 sparse dfm
     ... complete. 
  Elapsed time: 0.064 seconds.
     ... lowercasing
     ... indexing documents: 2 documents
     ... indexing features: 14 feature types
     ... applying a dictionary consisting of 6 keys
     ... created a 2 x 15 sparse dfm
     ... complete. 
  Elapsed time: 0.093 seconds.
     ... lowercasing
     ... indexing documents: 2 documents
     ... indexing features: 14 feature types
     ... applying a dictionary consisting of 6 keys
     ... created a 2 x 15 sparse dfm
     ... complete. 
  Elapsed time: 0.04 seconds.
  NULL
  1. Error: read a dictionary with NA as a key (@test-dictionaries.R#62) ---------
  'fmt' is not a character vector
  1: expect_true("NA" %in% names(testdict$SOUTH)) at testthat/test-dictionaries.R:62
  2: expect(identical(as.vector(object), TRUE), sprintf("%s isn't true.", lab), info = info)
  3: as.expectation(exp, ..., srcref = srcref)
  4: identical(as.vector(object), TRUE)
  5: as.vector(object)
  6: "NA" %in% names(testdict$SOUTH)
  7: testdict$SOUTH
  8: `$.dictionary`(testdict, SOUTH)
  9: abort(sprintf(.subset2(x, "lookup_msg"), name))
  10: cnd_error(type, .msg = msg, .call = sys.call(-1))
  11: new_cnd(c(.type, "error"), ..., .msg = .msg)
  12: is_null(.msg)
  13: typeof(x)
  14: sprintf(.subset2(x, "lookup_msg"), name)
  
  2. Failure: indexing for dictionary objects works (@test-dictionaries.R#113) ---
  names(testdict[1]) not equal to "CULTURE".
  target is NULL, current is character
  
  
  3. Failure: indexing for dictionary objects works (@test-dictionaries.R#114) ---
  names(testdict[[1]][1]) not equal to "CULTURE-HIGH".
  target is NULL, current is character
  
  
  4. Failure: indexing for dictionary objects works (@test-dictionaries.R#115) ---
  names(testdict[2]) not equal to "ECONOMY".
  target is NULL, current is character
  
  
  5. Failure: indexing for dictionary objects works (@test-dictionaries.R#116) ---
  names(testdict[[2]][1]) not equal to "+STATE+".
  target is NULL, current is character
  
  
  6. Failure: indexing for dictionary objects works (@test-dictionaries.R#118) ---
  print(testdict) does not match "Dictionary object with 9 primary key entries and 2 nested levels".
  Actual value: "# A dictionary: 0 objects"
  
  
  7. Failure: indexing for dictionary objects works (@test-dictionaries.R#122) ---
  print(testdict[1]) does not match "Dictionary object with 1 primary key entry and 2 nested levels".
  Actual value: "# A dictionary: 0 objects"
  
  
  8. Failure: indexing for dictionary keys works (@test-dictionaries.R#136) ------
  print(dict) does not match "Dictionary object with 2 key entries\\.".
  Actual value: "# A dictionary: 0 objects"
  
  
  9. Failure: indexing for dictionary keys works (@test-dictionaries.R#140) ------
  print(dict[1]) does not match "Dictionary object with 1 key entry\\.".
  Actual value: "# A dictionary: 0 objects"
  
  
  10. Failure: dictionary_depth works correctly (@test-dictionaries.R#156) -------
  print(dict2) does not match "Dictionary object with 2 primary key entries and 2 nested levels\\.".
  Actual value: "# A dictionary: 0 objects"
  
  
  NULL
  NULL
  Creating a dfm from a corpus ...
     ... tokenizing texts
     ... lowercasing
     ... found 14 documents, 5,058 features
     ... created a 14 x 5,058 sparse dfm
     ... complete. 
  Elapsed time: 0.001 seconds.
  Creating a dfm from a corpus ...
     ... tokenizing texts
     ... lowercasing
     ... found 14 documents, 5,058 features
     ... created a 14 x 5,058 sparse dfm
     ... complete. 
  Elapsed time: 0.001 seconds.
  remove_numbers = FALSE 
  testthat results ================================================================
  OK: 949 SKIPPED: 10 FAILED: 10
  1.  Error: read a dictionary with NA as a key (@test-dictionaries.R#62) 
  2.  Failure: indexing for dictionary objects works (@test-dictionaries.R#113) 
  3.  Failure: indexing for dictionary objects works (@test-dictionaries.R#114) 
  4.  Failure: indexing for dictionary objects works (@test-dictionaries.R#115) 
  5.  Failure: indexing for dictionary objects works (@test-dictionaries.R#116) 
  6.  Failure: indexing for dictionary objects works (@test-dictionaries.R#118) 
  7.  Failure: indexing for dictionary objects works (@test-dictionaries.R#122) 
  8.  Failure: indexing for dictionary keys works (@test-dictionaries.R#136) 
  9.  Failure: indexing for dictionary keys works (@test-dictionaries.R#140) 
  10. Failure: dictionary_depth works correctly (@test-dictionaries.R#156) 
  
  Error: testthat unit tests failed
  Execution halted